文献类型：期刊文献

英文题名：Assessment of human operator functional state using a novel differential evolution optimization based adaptive fuzzy model

作者：Wang, Raofen[1];Zhang, Jianhua[1];Zhang, Yu[1];Wang, Xingyu[1]

机构：[1]E China Univ Sci & Technol, Lab Brain Comp Interfaces & Control, Shanghai 200237, Peoples R China

年份：2012

卷号：7

期号：5

起止页码：490

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20122915252981);WOS:【SCI-EXPANDED(收录号:WOS:000307135800009)】；

基金：The authors would like to thank the editor and anonymous referees for their valuable comments. They would also like to thank Dr. Bei Wang for her English correction and advice. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61074113, 60775033 and 61075070, Shanghai Leading Academic Discipline Project B504, and Fundamental Research Funds for the Central Universities WH0914028. The 2nd author (J. Zhang) would like to gratefully acknowledge Professor D Manzey, TU Berlin, Germany for providing the AUTOCAMS software which made the data collection experiments essential for this work possible.

语种：英文

外文关键词：Operator functional state; Psychophysiological measure; Adaptive-Network-based Fuzzy Inference System; Differential evolution; Ant colony search

摘要：With the development of human-machine systems, there has been a growing concern about the consequences of operator performance breakdown under excessive level of workload, especially in safety-critical situations. Assessment and detection of the operator functional state (OFS) enable us to predict the high operational risks of operator. This paper adopts the psychophysiological signals and task performance measures to evaluate OFS under different levels of mental workload. Four indices extracted from electrocardiogram and electroencephalogram, including heart rate (HR), ratio of the standard deviation to the average of HR segment, task load indices (TLI1 and TLI2), are chosen as the inputs of the proposed model. A technique of differential evolution with ant colony search (DEACS) is developed to optimize the parameters of Adaptive-Network-based Fuzzy Inference System (ANFIS). The optimized ANFIS model is employed to estimate the OFS under a series of process control tasks on a simulated software platform of AUTOmation-enhanced Cabin Air Management System. The results showed that the proposed adaptive fuzzy model based on ANFIS and DEACS algorithm is applicable for the operator functional state assessment. (C) 2011 Elsevier Ltd. All rights reserved.